Data Integration in Poplar: ‘Omics Layers and Integration Strategies

Weighill, Deborah; Tschaplinski, Timothy J.; Tuskan, Gerald A.; Jacobson, Daniel

doi:10.3389/fgene.2019.00874

Cited by 21 publications

(15 citation statements)

References 193 publications

(273 reference statements)

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“…A second motivation was to demonstrate a methodology for combining large datasets in anticipation of a new generation of analytical tools. The newly emerging ability to model genetic responses to environmental attributes presented as continuous surfaces ( Resende et al 2020 ), to predict unobserved phenotypes through environmental relationship matrices ( Jarquín et al 2014 ), and to synthesize large ‘omics datasets into coherent models of plant function ( Weighill et al 2019 ) rely on very large experimental populations distributed across the landscape. The most informed results from these alternative analytical approaches may be derived from datasets that include structured genetic effects, such as those managed in formal breeding programs evaluating diverse sets of germplasm across a wide range of environments.…”

Section: Introductionmentioning

confidence: 99%

Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill

Callister

Bradshaw²,

Elms³

et al. 2021

G3 Genes|Genomes|Genetics

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Single-step GBLUP (HBLUP) efficiently combines genomic, pedigree, and phenotypic information for holistic genetic analyses of disjunct breeding populations. We combined data from two independent multi-generational Eucalyptus globulus breeding populations to provide direct comparisons across the programs and indirect predictions in environments where pedigreed families had not been evaluated. Despite few known pedigree connections between the programs, genomic relationships provided the connectivity required to create a unified relationship matrix, H, which was used to compare pedigree-based and HBLUP models. Stem volume data from 48 sites spread across three regions of southern Australia and wood quality data across 20 sites provided comparisons of model accuracy. Genotyping proved valuable for correcting pedigree errors and HBLUP more precisely defines relationships within and among populations, with relationships among the genotyped individuals used to connect the pedigrees of the two programs. Cryptic relationships among the native range populations provided evidence of population structure and evidence of the origin of landrace populations. HBLUP across programs improved prediction accuracy of parents and genotyped individuals and enabled breeding value predictions to be directly compared and inferred in regions where little to no testing has been undertaken. The impact of incorporating genetic groups in the estimation of H will further align traditional genetic evaluation pipelines with approaches that incorporate marker derived relationships into prediction models.

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Section: Introductionmentioning

confidence: 99%

Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill

Callister

Bradshaw²,

Elms³

et al. 2021

G3 Genes|Genomes|Genetics

View full text Add to dashboard Cite

show abstract

“…As a more visual representation of this, network-based integration involves individual objects of a system interacting with each other along "edges." This is best understood as nodes connected via lines in which lines are edges and nodes are objects in the system (Weighill et al 2019). As an extension of this concept of networks, modularity typically operates such that a given discipline, focus area, or subtask constitutes a module, and that module can be connected to other modules via any number of realistic interdependencies in the systems of interest.…”

Section: Attempts At Data Integrationmentioning

confidence: 99%

“…The second method, "signal-based integration," involves the tracking of a variable as it correlates to changing inputs (Weighill et al 2019). For the third, Tobi (2014) proposed the formation of a single complex attribute based on the implementation of portfolio representation of measurement, noted in Fig.…”

Section: Attempts At Data Integrationmentioning

confidence: 99%

Barriers and Possibilities for Interdisciplinary Disaster Science Research: Critical Appraisal of the Literature

Johnston

Lindt

2022

Nat. Hazards Rev.

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The strength and speed of modeling software has increased drastically in recent years. As it does so, researchers across a variety of fields work to determine how most effectively to utilize this strength and speed. Many of them have turned to interdisciplinarity as a means of creating more representative models. This is the case for disaster research as this area of interest involves many intricately interdependent systems. In working on interdisciplinary projects, past research has noted several barriers to complete integration, including differences in language and methodology, institutional structures not conducive to interdisciplinary collaboration, and nuanced tension between disciplines. Many solutions to these issues have been presented: facilitated conversation, increased institutional support, and several others. However, one area of difficulty for which comprehensive solutions have not yet been realized is data integration. This is indeed a challenge that lays at the heart of meaningful interdisciplinarity. The data are frequently telling an intricately interwoven story, and the more these data can be analyzed in a cohesive manner the more likely it is that researchers will be able to harness their predictive power to reduce disasters. In order to understand what efforts have been made at data integration, 29 papers are systematically reviewed in order to extract the nature of previous attempts, reasons for integration, challenges, shortcomings, and recommendations for future work. The papers analyzed most commonly referenced the different syntax and data types as a challenge of integration. Regarding shortcomings of integration efforts, the most common concern was that of model parameterization bias and substantial uncertainties. As a recommendation for future work, the papers most commonly suggested more standardization of data and methods across collaborating disciplines and from one project to the next in order to avoid these shortcoming and challenges.

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“…A deeper understanding of the genetic basis of addiction will require considering gene × gene and gene × environment interactions and developing methods for doing so. Recent analytical and technical advancements are beginning to allow us to look not just at highly interactive genetic effects [Joubert et al, 2018[Joubert et al, , 2019, but also at multiple "omic" levels simultaneously [Weighill et al, 2019]. These multiple omic levels include the genome, the epigenome, the transcriptome, and the proteome, among others.…”

Section: Moving Forward In the Post-gwas Eramentioning

confidence: 99%

The Streetlight Effect: Reappraising the Study of Addiction in Light of the Findings of Genome-wide Association Studies

2020

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Drug dependence has long been thought to have a genetic component. Research seeking to identify the genetic basis of addiction has gone through important transitions over its history, in part based upon the emergence of new technologies, but also as the result of changing perspectives. Early research approaches were largely dictated by available technology, with technological advancements having highly transformative effects on genetic research, but the limitations of technology also affected modes of thinking about the genetic causes of disease. This review explores these transitions in thinking about the genetic causes of addiction in terms of the “streetlight effect,” which is a type of observational bias whereby people search for something only where it is easiest to search. In this way, the genes that were initially studied in the field of addiction genetics were chosen because they were the most “obvious,” and formed current understanding of the biological mechanisms underlying the actions of drugs of abuse and drug dependence. The problem with this emphasis is that prior to the genomic era the vast majority of genes and proteins had yet to be identified, much less studied. This review considers how these initial choices, as well as subsequent choices that were also driven by technological limitations, shaped the study of the genetic basis of drug dependence. While genome-wide approaches overcame the initial biases regarding which genes to choose to study inherent in candidate gene studies and other approaches, genome-wide approaches necessitated other assumptions. These included additive genetic causation and limited allelic heterogeneity, which both appear to be incorrect. Thus, the next stage of advancement in this field must overcome these shortcomings through approaches that allow the examination of complex interactive effects, both gene × gene and gene × environment interactions. Techniques for these sorts of studies have recently been developed and represent the next step in our understanding of the genetic basis of drug dependence.

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Data Integration in Poplar: ‘Omics Layers and Integration Strategies

Cited by 21 publications

References 193 publications

Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill

Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill

Barriers and Possibilities for Interdisciplinary Disaster Science Research: Critical Appraisal of the Literature

The Streetlight Effect: Reappraising the Study of Addiction in Light of the Findings of Genome-wide Association Studies

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